Recommendations in visual analytics using emotions : a mixed-initiative interaction approach

The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Panwar, Prateek

Advisor dc:contributor.advisor

• Collins, Christopher

• Affective computing
• Information visualization
• Recommendation system
• Eye tracking
• GSR

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/956

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/956